{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "competent-assist",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/.pyenv/versions/3.7.0/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.tsa import stattools\n",
    "from statsmodels.graphics.tsaplots import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "biblical-patch",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>preclose</th>\n",
       "      <th>vol</th>\n",
       "      <th>value</th>\n",
       "      <th>ret</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04T00:00:00Z</th>\n",
       "      <td>HSI</td>\n",
       "      <td>21860.04</td>\n",
       "      <td>22024.83</td>\n",
       "      <td>21689.22</td>\n",
       "      <td>21823.28</td>\n",
       "      <td>21872.50</td>\n",
       "      <td>48509170000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.002250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05T00:00:00Z</th>\n",
       "      <td>HSI</td>\n",
       "      <td>22092.15</td>\n",
       "      <td>22297.04</td>\n",
       "      <td>21987.27</td>\n",
       "      <td>22279.58</td>\n",
       "      <td>21823.28</td>\n",
       "      <td>82973500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.020909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06T00:00:00Z</th>\n",
       "      <td>HSI</td>\n",
       "      <td>22357.46</td>\n",
       "      <td>22514.79</td>\n",
       "      <td>22277.13</td>\n",
       "      <td>22416.67</td>\n",
       "      <td>22279.58</td>\n",
       "      <td>91328340000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07T00:00:00Z</th>\n",
       "      <td>HSI</td>\n",
       "      <td>22548.03</td>\n",
       "      <td>22548.03</td>\n",
       "      <td>22169.61</td>\n",
       "      <td>22269.45</td>\n",
       "      <td>22416.67</td>\n",
       "      <td>79167640000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.006567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08T00:00:00Z</th>\n",
       "      <td>HSI</td>\n",
       "      <td>22282.75</td>\n",
       "      <td>22443.22</td>\n",
       "      <td>22206.16</td>\n",
       "      <td>22296.75</td>\n",
       "      <td>22269.45</td>\n",
       "      <td>71931720000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     symbol      open      high       low     close  preclose  \\\n",
       "time                                                                            \n",
       "2010-01-04T00:00:00Z    HSI  21860.04  22024.83  21689.22  21823.28  21872.50   \n",
       "2010-01-05T00:00:00Z    HSI  22092.15  22297.04  21987.27  22279.58  21823.28   \n",
       "2010-01-06T00:00:00Z    HSI  22357.46  22514.79  22277.13  22416.67  22279.58   \n",
       "2010-01-07T00:00:00Z    HSI  22548.03  22548.03  22169.61  22269.45  22416.67   \n",
       "2010-01-08T00:00:00Z    HSI  22282.75  22443.22  22206.16  22296.75  22269.45   \n",
       "\n",
       "                              vol  value       ret  \n",
       "time                                                \n",
       "2010-01-04T00:00:00Z  48509170000    0.0 -0.002250  \n",
       "2010-01-05T00:00:00Z  82973500000    0.0  0.020909  \n",
       "2010-01-06T00:00:00Z  91328340000    0.0  0.006153  \n",
       "2010-01-07T00:00:00Z  79167640000    0.0 -0.006567  \n",
       "2010-01-08T00:00:00Z  71931720000    0.0  0.001226  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hsi = pd.read_csv('datasets/hsi.csv', index_col='time')\n",
    "df_hsi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "historic-judgment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time\n",
       "2010-01-04T00:00:00Z   -0.002250\n",
       "2010-01-05T00:00:00Z    0.020909\n",
       "2010-01-06T00:00:00Z    0.006153\n",
       "2010-01-07T00:00:00Z   -0.006567\n",
       "2010-01-08T00:00:00Z    0.001226\n",
       "Name: ret, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# acf   pacf 计算自相关判断\n",
    "hsi_ret = df_hsi.ret\n",
    "hsi_ret.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "boxed-flight",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/tsa/stattools.py:662: FutureWarning: The default number of lags is changing from 40 tomin(int(10 * np.log10(nobs)), nobs - 1) after 0.12is released. Set the number of lags to an integer to  silence this warning.\n",
      "  FutureWarning,\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/tsa/stattools.py:671: FutureWarning: fft=True will become the default after the release of the 0.12 release of statsmodels. To suppress this warning, explicitly set fft=False.\n",
      "  FutureWarning,\n"
     ]
    }
   ],
   "source": [
    "acfs= stattools.acf(hsi_ret)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "deadly-worry",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        , -0.0035981 ,  0.01246954,  0.00793257, -0.02535696,\n",
       "        0.00739616, -0.03033115,  0.01948345, -0.01500197,  0.01478623,\n",
       "       -0.01187436, -0.01876684,  0.00112679,  0.00861361,  0.00517306,\n",
       "       -0.0362903 ,  0.01848701,  0.00196739, -0.02205431,  0.00727216,\n",
       "       -0.01281086,  0.02244614, -0.03839736, -0.02476061, -0.03469165,\n",
       "        0.01231128,  0.00568932, -0.03237226,  0.00413834, -0.00659387,\n",
       "       -0.01478561, -0.00872847,  0.02317909,  0.0177139 , -0.0077293 ,\n",
       "        0.02134155, -0.0071198 ,  0.04129035,  0.0039187 ,  0.01254224,\n",
       "        0.00978706])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "quiet-stuff",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/tsa/stattools.py:1029: FutureWarning: The default number of lags is changing from 40 tomin(int(10 * np.log10(nobs)), nobs // 2 - 1) after 0.12is released. Set the number of lags to an integer to  silence this warning.\n",
      "  FutureWarning,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 1.00000000e+00, -3.59939413e-03,  1.24657126e-02,  8.03170708e-03,\n",
       "       -2.54973609e-02,  7.03548522e-03, -2.97953953e-02,  1.95969529e-02,\n",
       "       -1.49856453e-02,  1.51789278e-02, -1.34371046e-02, -1.76019724e-02,\n",
       "       -8.70225696e-04,  1.14827151e-02,  3.42865756e-03, -3.60872528e-02,\n",
       "        1.69430266e-02,  3.16973578e-03, -2.18539977e-02,  5.39381343e-03,\n",
       "       -1.07583967e-02,  2.03078885e-02, -3.79847844e-02, -2.62870022e-02,\n",
       "       -3.48272458e-02,  1.49906401e-02,  2.09293286e-03, -3.03572766e-02,\n",
       "       -1.04093509e-03, -4.81354722e-03, -1.89171678e-02, -6.46323380e-03,\n",
       "        2.38476726e-02,  1.42254911e-02, -9.27510454e-03,  1.81128365e-02,\n",
       "       -3.03545742e-03,  3.98256990e-02,  2.11957522e-03,  1.30577668e-02,\n",
       "        8.90646171e-03])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pacfs = stattools.pacf(hsi_ret) # 偏自相关系数\n",
    "pacfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "equal-button",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf(hsi_ret, use_vlines=True, lags=30)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "minute-symbol",
   "metadata": {},
   "source": [
    "只有基于平稳时间序列的预测才是有效的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "advanced-discount",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9ca6f8ac50>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hsiClose = df_hsi.close\n",
    "hsiClose.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "electronic-engine",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9ca3e2cdd8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hsi_ret.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "pacific-passage",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf(hsi_ret, use_vlines=True, lags=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "saving-premises",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pacf(hsi_ret, use_vlines=True, lags=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "powered-priest",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aLZJQrafrA/jHH794TDdJ3ngt7dN+8Uhlnva+XBz4ZgYc2/JuDE42wrtWTybWV7NW+cHRKgA/3Hlgxs9uhPxMYW9zz4Fv1qeSJKjkwrpST6jUEiLgyX2HJwYyjwb60TBudXDSekshgS/pIuCPgEHgf0fEp6as/xBwLVAD9gH/PiKeKWLbZmURkQV4FtLVRlhPucqkUaZZa3qsmnaZ7D04Pt+7b12g7cCXNAjcDLwd2AlskbQpIh7JFXsAGIqIEUn/AfhD4J3tbtus1zV+fJMP8Px0o+97y9MvTmqBT1WWq0ysPUW08M8HtkfEUwCS7gAuAyYCPyK+myt/L/CuArZr1rXGqvVJwV2pTQ7yw+M1ItJBzJk0Wukzhb1Zq4oI/DOBHbn5ncAFM5S/BvibZiskbQA2AKxbt66AXTMrTgSMVGpUa2nXSj7IK1mYN677fuAnwzN+VuLBS+uAeR20lfQuYAj4uWbrI2IjsBFgaGjI/yJsXjSCvFI7GtwTQZ4tOzhWhYCHdsx8NYqv+7ZuVkTg7wLW5ubXZMsmkfQ24GPAz0WER4xsXoxV6xNXp+RDfDybbjXIcY5bHygi8LcAZ0s6izTorwB+JV9A0uuBPwUuioi9BWzTSq6exESIj9frE9ONcG+1a8VBbmXSduBHRE3SdcDdpJdl3hIR2yTdBGyNiE3AfwVOBr6a3fLzJxFxabvbtv4UEYxnrfC0NV6fCPNWBzvdtWJ2rEL68CNiM7B5yrIbctNvK2I71h9q9XyYp4E+WqmTRHD/My9SqU0f1h7sNDtx/qWtFa4R6I1QTyK9GdZ4rc54LWl6iWG1nl5HPlPYm1l7HPg2a9F4ak/AnoNjjFXTIB+vpq31ai7QG7/s9M2wzDrPgW9NBXBkvDYR5mPVOmPVhLGsP/1w9ovOp45ze1oz6x4O/BJLkmCslgb5aLXOWDXtS29c4XK8OyCaWW9x4JfAeK3OWOVof/qjuw8yWq1P3DlxKl/hYtafHPh9pNFCH63WGalkLfZqfWKQtNGfPjxS7eRumlmHOPB7UOMyxpFKnZFKjSPjNeqt/MjIzErNgd/FgrSffe/BMY5k4T5amXwVDPgpQmbWGgd+l6gnwZGstX5kvMbh8TqHsvu8HO9BzWZmrXDgd0Cj5b77wOhEuI9lTyw6pqCZWUEc+PMgvZlXlUNjtewvbbk//fxIp3fNzErEgT8HRipHg/3gWI3xajK5gFvuZtYBDvwCjFXrHBitMlKpU0/i+PdWNzPrAAf+CYgIDo7VGB6pMDySBj2kNw0zM+tWDvwWRcDeg2MMj1Y5MFr1Q6XNrOc48KfRaMUfGKmmP2xKwpdHmllPc+DnNPrih0eqHBw72or3D5vMrB+UPvBrSfD080cYHq0ymvXFm5n1o9IF/milzvBoZaIVT8DuA2Od3i0zszlXmsA/OFZl+97Dk6+Jd0+NmZXIQKd3YL5UskfwmZmVVWkC38ys7AoJfEkXSXpM0nZJ1zdZv1jSndn6+yStL2K7ZmbWurYDX9IgcDNwMXAucKWkc6cUuwbYHxGvAj4L/EG72zUzs9lRu88vlfQm4MaI+MVs/iMAEfH7uTJ3Z2W+L2kB8BywKmbY+GmvOCfe/tFbTmifHnzoQQDOe915E8uq9fRB3XlPPPIwAGef+9oZP8/lumObLudyZSl36pKFM75vJne971/cHxFDzdYVEfjvAC6KiGuz+XcDF0TEdbkyD2dldmbzT2Zlnp/yWRuADQAnr37lP7/kE7e1tW95zQLfzKwbzVXgd9VlmRGxEdgIMDQ0FHf++psK++znD4/zxJ7DhX2emdlcueCs0xgY0Am99673Tb+uiEHbXcDa3PyabFnTMlmXznLghQK2bWZmLSoi8LcAZ0s6S9Ii4Apg05Qym4Crsul3AP9vpv57MzMrXttdOhFRk3QdcDcwCNwSEdsk3QRsjYhNwBeA2yRtB14krRTMzGweFdKHHxGbgc1Tlt2Qmx4D/l0R2zIzsxPTVYO2c2nlyYtZunCQ4dEqwyMVDo3VcKeSmZVJaQIfYNniBSxbvIAzVyylnkR27/sKw6NV32fHzPpeqQI/b3BAnLZsEactWwRMuW3yaBU/88TM+k1pA3+qpYsGWbpoKauXLyVJgoNj6ZOv/GAUM+sXDvwmBgbEipMWseKktPXfePTh/pEKB0drfuShmfUkB34LliwcZMnCQU4/dcmk1v/+kQpj7vs3sx7hwJ+lfOt/PcsYq9bZP+K+fzPrfg78Ni1ZOMjq5Uf7/g+N1zg0VuXQWI3D4zVqddcAZtYdHPgFGhgQy5cuZPnS9E53EcFIpc6hsbQSODhWo1JzF5CZdYYDfw5Jmrj2/+XLlwDpAPDh8RpHxmvZa92DwGY2Lxz486wxALzy5MUTy0YraSUwUnElYGZzx4HfBdLfAAwCx1YCo5U6I9W0EnB3kJm1w4HfpY5WAkfV6glHKvW0EqjUGKnUGan4bMDMWuPA7yELBgdYvnRgYlC4YayaVgKj1ewvm/YVQmaW58DvA41xgZdMWV6pJU0rAncNmZWTA7+PLVowwKIFAyxn8hlBPQnGqvX0zKBaZ6yaTMxXfVZg1rcc+CU0OHD0ctGpqvVG+Kev47V0erxWp1JzZWDWyxz4NsnCwQEWDg5wypJj1+XPDMZqCePVfGWQ+LYSZl3OgW8tm+nMICKo1JOJCmC8mjBeSybOEKr1xE8YM+swB74VQhKLFwyyeMEgTBkzgLRCSCuAoxVCpZ5kFYPPEMzmgwPf5oWkiauJmlUIkF5V1Aj/RmVw9NVjCGbtcuBb12hcVTSdJEm7jcZryUSlUKnl/lwpmM2orcCXdBpwJ7AeeBq4PCL2TylzHvB54FSgDvxuRNzZznatnAYGxJKBxllCc41KIV8ZVLPp8aySqLr7yEqq3Rb+9cB3IuJTkq7P5j88pcwI8J6IeELSGcD9ku6OiOE2t212jFYqBWCiEmi8VibmY2LeA83Wb9oN/MuAC7PpW4F7mBL4EfF4bvpZSXuBVYAD3zqmcfnpTCKCaj0mVw71ZNIynzFYL2k38E+PiN3Z9HPA6TMVlnQ+sAh4cpr1G4ANAOvWrWtz18zaI4lFC8SiBQMsWzxz2VpWETTODKr1hGptynxWxmcN1inHDXxJ3wZe3mTVx/IzERGSpv1fWdJq4DbgqohoejOXiNgIbAQYGhryPwvrGQsGB1gwCEuZuSspf9ZQm1pBZMvz064crEjHDfyIeNt06yTtkbQ6InZngb53mnKnAn8NfCwi7j3hvTXrcfmzhlbkK4balEqhlqTjDbXkaAViNpN2u3Q2AVcBn8pe/2pqAUmLgL8EvhQRX2tze2alko41HP/MAdIrlKpJGvwTlUSSrzBioqJwBVFO7Qb+p4C7JF0DPANcDiBpCHhfRFybLXsL8FJJV2fvuzoiHmxz22aWMzAgFg8M0uTOF001updqSXrmUJty1tA4o8iv9+B0b2sr8CPiBeCtTZZvBa7Npr8MfLmd7ZhZ8Sa6l2itewnSG+g1zhLyFUKtHq4keoB/aWtmLRscEIMDx+9eypva1VRLJncr1ZJjK41a4quZ5oID38zm1Gy7mhoawZ+vBBrjEPXkaMXhiqJ1Dnwz60qNS11nq9HtVE+OnlHUk6CaBPX65Iqinp19NMr2Owe+mfWVE+l2gnQQu5ZEdvYwpXKYprJoVCa9cmbhwDczIx3EXjgojnMbpmlNOrNIgiQJ6pG+JsHEdD0Jkkj/6gnZa+Reiz2uPAe+mVkBTvTMYj61fj2WmZn1NAe+mVlJOPDNzErCgW9mVhIOfDOzknDgm5mVhAPfzKwkHPhmZiXhwDczKwlFl94AQtI+0oeqnKiVwPMF7U4n9cNx9MMxgI+j2/g4mntFRKxqtqJrA79dkrZGxFCn96Nd/XAc/XAM4OPoNj6O2XOXjplZSTjwzcxKop8Df2Ond6Ag/XAc/XAM4OPoNj6OWerbPnwzM5usn1v4ZmaW48A3MyuJvgt8SRdJekzSdknXd3p/TpSkpyX9SNKDkrZ2en9aJekWSXslPZxbdpqkb0l6Int9SSf3sRXTHMeNknZl38mDki7p5D62QtJaSd+V9IikbZI+kC3vqe9khuPoqe9E0hJJ/yjpoew4PpktP0vSfVlu3Slp0Zxsv5/68CUNAo8Dbwd2AluAKyPikY7u2AmQ9DQwFBE99cMSSW8BDgNfiojXZsv+EHgxIj6VVcIviYgPd3I/j2ea47gROBwR/62T+zYbklYDqyPiB5JOAe4Hfgm4mh76TmY4jsvpoe9EkoBlEXFY0kLg74EPAB8C/iIi7pD0J8BDEfH5orffby3884HtEfFURFSAO4DLOrxPpRIR3wNenLL4MuDWbPpW0n+oXW2a4+g5EbE7In6QTR8CHgXOpMe+kxmOo6dE6nA2uzD7C+Dnga9ly+fs++i3wD8T2JGb30kP/k+RCeCbku6XtKHTO9Om0yNidzb9HHB6J3emTddJ+mHW5dPV3SBTSVoPvB64jx7+TqYcB/TYdyJpUNKDwF7gW8CTwHBE1LIic5Zb/Rb4/eTNEfEG4GLgN7Iuhp4XaR9ir/Yjfh54JXAesBv4dGd3p3WSTga+DnwwIg7m1/XSd9LkOHruO4mIekScB6wh7ZX4J/O17X4L/F3A2tz8mmxZz4mIXdnrXuAvSf/H6FV7sj7YRl/s3g7vzwmJiD3ZP9YE+F/0yHeS9RV/HfjziPiLbHHPfSfNjqNXvxOAiBgGvgu8CVghaUG2as5yq98CfwtwdjbivQi4AtjU4X2aNUnLsoEpJC0DfgF4eOZ3dbVNwFXZ9FXAX3VwX05YIyAz/4Ye+E6yQcIvAI9GxGdyq3rqO5nuOHrtO5G0StKKbHop6QUmj5IG/zuyYnP2ffTVVToA2WVZnwMGgVsi4nc7vEuzJumnSFv1AAuAr/TKcUi6HbiQ9Jave4BPAN8A7gLWkd7y+vKI6OoB0WmO40LSroMAngZ+PdcP3pUkvRn4O+BHQJIt/ihp/3fPfCczHMeV9NB3IumfkQ7KDpI2uO+KiJuyf/N3AKcBDwDviojxwrffb4FvZmbN9VuXjpmZTcOBb2ZWEg58M7OScOCbmZWEA9/MrCQc+GZmJeHANzMrif8P62q/w0JAeEgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf(hsiClose, use_vlines=True, lags=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "lasting-antenna",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
